from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax.struct.dataclass
class SCREAMING_SNAKE_CASE_		:
   # setable values
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				None
   __lowerCAmelCase           =				None  # sigma(t_i)







   @classmethod
   def 						lowerCamelCase_	(     cls						:						Optional[Any]				):





    """simple docstring"""


    return cls()





@dataclass
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				42
   __lowerCAmelCase           =				42
   __lowerCAmelCase           =				42





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase  ,     __lowerCAmelCase		):







   @property
   def 						lowerCamelCase_	(     self						:						Optional[Any]				):





    """simple docstring"""


    return True







   @register_to_config
   def __init__(     self						:						int   ,   lowerCamelCase_						:						float = 0.0_2   ,   lowerCamelCase_						:						float = 100   ,   lowerCamelCase_						:						float = 1.0_0_7   ,   lowerCamelCase_						:						float = 80   ,   lowerCamelCase_						:						float = 0.0_5   ,   lowerCamelCase_						:						float = 50   ,   ):





    """simple docstring"""


    pass







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    return KarrasVeSchedulerState.create()







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						KarrasVeSchedulerState   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Tuple = ()				):





    """simple docstring"""


    UpperCamelCase			  =      jnp.arange(0   ,   lowerCamelCase_				)[::-1].copy()
    UpperCamelCase			  =      [
        (
            self.config.sigma_max**2
            * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
        )
        for i in timesteps
    ]

    return state.replace(
        num_inference_steps=lowerCamelCase_   ,   schedule=jnp.array(lowerCamelCase_   ,   dtype=jnp.floataa				)   ,   timesteps=lowerCamelCase_   ,   )







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						KarrasVeSchedulerState   ,   lowerCamelCase_						:						jnp.ndarray   ,   lowerCamelCase_						:						float   ,   lowerCamelCase_						:						random.KeyArray   ,   ):





    """simple docstring"""


    if self.config.s_min <= sigma <= self.config.s_max:
     UpperCamelCase			  =      min(self.config.s_churn / state.num_inference_steps   ,   2**0.5 - 1				)
    else:
     UpperCamelCase			  =      0

    # sample eps ~ N(0, S_noise^2 * I)
    UpperCamelCase			  =      random.split(lowerCamelCase_   ,   num=1				)
    UpperCamelCase			  =      self.config.s_noise * random.normal(key=lowerCamelCase_   ,   shape=sample.shape				)
    UpperCamelCase			  =      sigma + gamma * sigma
    UpperCamelCase			  =      sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

    return sample_hat, sigma_hat







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						KarrasVeSchedulerState   ,   lowerCamelCase_						:						jnp.ndarray   ,   lowerCamelCase_						:						float   ,   lowerCamelCase_						:						float   ,   lowerCamelCase_						:						jnp.ndarray   ,   lowerCamelCase_						:						bool = True   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      sample_hat + sigma_hat * model_output
    UpperCamelCase			  =      (sample_hat - pred_original_sample) / sigma_hat
    UpperCamelCase			  =      sample_hat + (sigma_prev - sigma_hat) * derivative

    if not return_dict:
     return (sample_prev, derivative, state)

    return FlaxKarrasVeOutput(prev_sample=lowerCamelCase_   ,   derivative=lowerCamelCase_   ,   state=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						KarrasVeSchedulerState   ,   lowerCamelCase_						:						jnp.ndarray   ,   lowerCamelCase_						:						float   ,   lowerCamelCase_						:						float   ,   lowerCamelCase_						:						jnp.ndarray   ,   lowerCamelCase_						:						jnp.ndarray   ,   lowerCamelCase_						:						jnp.ndarray   ,   lowerCamelCase_						:						bool = True   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      sample_prev + sigma_prev * model_output
    UpperCamelCase			  =      (sample_prev - pred_original_sample) / sigma_prev
    UpperCamelCase			  =      sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

    if not return_dict:
     return (sample_prev, derivative, state)

    return FlaxKarrasVeOutput(prev_sample=lowerCamelCase_   ,   derivative=lowerCamelCase_   ,   state=lowerCamelCase_				)







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						KarrasVeSchedulerState   ,   lowerCamelCase_						:						Tuple   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						List[Any]				):





    """simple docstring"""


    raise NotImplementedError()

import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
     import torch

     from transformers.generation import (
         MaxLengthCriteria,
         MaxNewTokensCriteria,
         MaxTimeCriteria,
         StoppingCriteriaList,
         validate_stopping_criteria,
     )
@require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):







   def 						lowerCamelCase_	(     self						:						Tuple   ,   lowerCamelCase_						:						Tuple				):





    """simple docstring"""


    UpperCamelCase			  =      3
    UpperCamelCase			  =      250

    UpperCamelCase			  =      ids_tensor((batch_size, length)   ,   lowerCamelCase_				)
    UpperCamelCase			  =      torch.ones((batch_size, length)   ,   device=lowerCamelCase_   ,   dtype=torch.float				) / length
    return input_ids, scores







   def 						lowerCamelCase_	(     self						:						Tuple				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      StoppingCriteriaList(
        [
            MaxLengthCriteria(max_length=10				),
            MaxTimeCriteria(max_time=0.1				),
        ]				)

    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxLengthCriteria(max_length=10				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    UpperCamelCase			  =      MaxNewTokensCriteria(start_length=5   ,   max_new_tokens=5				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(9				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(10				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      StoppingCriteriaList([criteria]				)
    self.assertEqual(criteria_list.max_length   ,   10				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      self._get_tensors(5				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1				)
    self.assertFalse(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)

    UpperCamelCase			  =      MaxTimeCriteria(max_time=0.1   ,   initial_timestamp=time.time() - 0.2				)
    self.assertTrue(criteria(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   10				)

    with self.assertWarns(lowerCamelCase_				):
     validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10				)]				)   ,   11				)

    UpperCamelCase			  =      validate_stopping_criteria(StoppingCriteriaList()   ,   11				)

    self.assertEqual(len(lowerCamelCase_				)   ,   1				)
